Semantic search engines perform an analysis of a query provided by a user to determine the user's intent and then use knowledge gathered from the analysis to retrieve relevant items. The rationale is sound: the more the search engine knows about the user's requirements, the better the search results are.
A semantic search engine for an e-commerce portal (or other electronic publication system), such as a classifieds website, network-based marketplace, or retailer with a product-based inventory, needs to understand the product or products the user is interested in after the user issues the query, which contains one or more keywords, to the search engine. One example challenge that may exist includes providing relevant search results. In other words, an accurate prediction algorithm may go a long a way to increasing the relevancy of the items that are fetched from the inventory. For instance, assume that whenever users issue a query “ipod,” the users are primarily interested in the iPod nano MPEG-1 Audio Layer 3 (MP3) player. In this case, all of the iPod accessories can be pruned away from the search results, even though the iPod accessories may contain the term “ipod.” Another example challenge that may exist includes providing high quality product recommendations. An efficient recommendation system fosters purchases, hence generating revenue. Understanding the user's intent may allow the recommendation engine to make higher quality recommendations.